GENERAL GAME-PLAYING AND REINFORCEMENT LEARNING |
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Authors: | Robert Levinson |
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Affiliation: | Department of Computer Science, University of California, Santa Cruz, CA 95060 E-mail: |
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Abstract: | This paper provides a blueprint for the development of a fully domain-independent single-agent and multiagent heuristic search system. It gives a graph-theoretic representation of search problems based on conceptual graphs and outlines two different learning systems. One, an "informed learner", makes use of the graph-theoretic definition of a search problem or game in playing and adapting to a game in the given environment. The other, a "blind learner", is not given access to the rules of a domain but must discover and then exploit the underlying mathematical structure of a given domain. Relevant work of others is referenced within the context of the blueprint. To illustrate further how one might go about creating general game-playing agents, we show how we can generalize the understanding obtained with the Morph chess system to all games involving the interactions of abstract mathematical relations. A monitor for such domains has been developed, along with an implementation of a blind and informed learning system known as Morphll. Performance results with MorphK are preliminary but encouraging and provide a few more data points with which to understand and evaluate the blueprint. |
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Keywords: | games mathematical structure heuristic search machine learning hypergraphs neural networks analogical reasoning RETE relational patterns hierarchical reinforcement learning |
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